Prioritizing health outcomes when assessing the effects of exposure to radiofrequency electromagnetic fields: A survey among experts
Bibliographic record
Abstract
Exposure to radiofrequency (RF) electromagnetic fields (EMF) (frequencies of 100 kHz to 300 GHz) has been steadily increasing. In addition to heat-related effects of RF EMF, other yet-unspecified biological effects, might exist which could possibly lead to health effects. Given the large number of health endpoints that have been studied, we wanted to prioritize those that would merit systematic reviews. We developed a survey listing of all health endpoints reported in the literature and we asked 300 RF EMF experts and researchers to prioritize these health effects for systematic review as critical, important or unimportant. We also asked the experts to provide the rationale for their prioritization. Of the 300 RF EMF experts queried, 164 (54%) responded. They rated cancer, heat-related effects, adverse birth outcomes, electromagnetic hypersensitivity, cognitive impairment, adverse pregnancy outcomes and oxidative stress as outcomes most critical regarding RF EMF exposure. For these outcomes, systematic reviews are needed. For heat-related outcomes, the experts based their ranking of the critical outcomes on what is known from human or animal studies, and for cancer and other outcomes, they based their rating also on public concern. To assess health risks of an exposure in a robust manner, it is important to prioritize the health outcomes that should be systematically reviewed. Here we have shown that it feasible to do so in an inclusive and transparent way.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".